Effects of ice number concentration on dynamics of a shallow mixed-phase stratiform cloud
Bibliographic record
Abstract
[1] Previous modeling studies have shown high sensitivity of mixed-phase clouds to ice number concentration, Ni, with simulated clouds often transitioning from mixed-phase to ice-only regime within a narrow range of Ni. To better understand the mechanisms behind this transition, we analyze several simulations of a mixed-phase stratiform Arctic cloud observed on 26 April 2008 during the Indirect and Semi-Direct Aerosol Campaign (ISDAC). In the BASE run, Ni is constrained to match the measured value and a persistent mixed-phase cloud is formed, with properties similar to those observed. When Ni is quadrupled (HI_ICE) the liquid water path is reduced by half within two hours. The changes in liquid water are accompanied by diminishing radiative cooling and slowing vertical mixing, exposing complex interactions among microphysics, radiation and dynamics. Deviations of BASE and HI_ICE from a simulation without ice are used to explore the linearity of the model response to variation in Ni. It is shown that early changes in cloud condensate amount and radiative cooling rate are proportional to Ni, while changes in the vertical buoyancy flux and dynamics are qualitatively different in HI_ICE compared to BASE. The nonlinear (with respect to Ni) reduction in buoyancy flux drives the initial response of the mixed layer dynamics to the appearance of ice and subsequently determines the sustainability of liquid water in the cloud in this case. Two additional sensitivity experiments link the decreased buoyancy production to the latent heat release from the depositional ice growth while confirming the importance of the cloud-radiation feedback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".